• Asia-Pacific Journal of Business
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• v.1 no.2
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• pp.83-106
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• 2010

The purpose of this study is to examine how perceived service quality influences perceived uncertainty, customer satisfaction, relation quality and loyalty, and in turn, provides insight for Korean banks when they penetrate into the Chinese marketplace. The findings are as followed. First, Perceived uncertainty has an important mediating role in the relation between perceived service quality and customer satisfaction. Second, perceived service quality has a direct effect on customer satisfaction, customer satisfaction has an important mediating role in the relationship between perceived service quality and relationship quality. Third, perceived uncertainty has a direct effect on customer satisfaction, but is significantly negative. Customer satisfaction has an important mediating role in the relationship between perceived uncertainty and relationship quality. Fourth, relationship quality has a direct effect on attitudinal loyalty and behavioral loyalty.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.495-505
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• 2006

In order to classify aerosol type, Aerosol Optical Thickness (AOT) and Fine mode Fraction (FF), which is the optical thickness ratio of small particles$(<1{\mu}m)$ to total particles, data from MODIS (MODerate Imaging Spectraradiometer) aerosol products were analyzed over North-East Asia during one year period of 2005. A study area was in the ocean region of $20^{\circ}N\sim50^{\circ}N$ and $110^{\circ}E\simt50^{\circ}E$. Three main atmospheric aerosols such as dust, sea-salt, and pollution can be classified by using the relationship between AOT and FF. Dust aerosol has frequently observed over the study area with relatively high aerosol loading (AOT>0.3) of large particles (FF<0.65) and its contribution to total AOT in spring was up to 24.0%. Pollution aerosol, which is originated from anthropogenic sources as well as a natural process like biomass burning, has observed in the regime of high FF (>0.65) with wide AOT variation. Average pollution AOT was $0.31{\pm}0.05$ and its contribution to total AOT was 79.8% in summer. Characteristic of sea-salt aerosol was identified with low AOT (<0.3), almost below 0.1, and slightly higher FF than dust and lower FF than pollution. Seasonal analysis results show that maximum AOT $(0.33{\pm}0.11)$ with FF $(0.66{\pm}0.21)$ in spring and minimum AOT $(0.19{\pm}0.05)$, FF $(0.60{\pm}0.14)$ in fall were observed in the study area. Spatial characteristic was that AOT increasing trend is observed as closing to the eastern part of China due to transport of aerosols from China by the prevailing westerlies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

• Journal of Chest Surgery
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• v.40 no.3 s.272
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• pp.159-167
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• 2007

Background: Vocabularies originating from Chinese characters constitute an important common factor in the medical terminologies used 3 eastern Asian countries; Korea, China and Japan. This study was performed to comparatively analyze the medical terminologies of these 3 countries in the field of cardiopulmonary bypass (CPB) and; thereby, facilitate further understanding among the 3 medical societies. Material and Method: A total of 129 English terms (core 85 and related 44) in the field of CPB were selected and translated into each country's official terminology, with help from Seoul National University Hospital (Korea), Tokyo Michi Memorial Hospital(Japan), and Yanbian Welfare Hospital and Harbin Children Hospital (China). Dictionaries and CPB textbooks were also cited. In addition to the official terminology used in each country, the frequency of use of English terms in a clinical setting was also analyzed. Result and Conclusion: Among the 129 terms, 28 (21.7%) were identical between the 3 countries, as based on the Chinese characters. 86 terms were identical between only two countries, mostly between Korea and Japan. As a result, the identity rate in CPB terminology between Korea and Japan was 86.8%; whereas, between Korea and China and between Japan and China the rates were both 24.8%. The frequency of use of English terms in clinical practices was much higher in Korea and Japan than in China. Despite some inherent limitations involved in the analysis, this study can be a meaningful foundation in facilitating mutual understanding between the medical societies of these 3 eastern Asian countries.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.271-283
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• 2012

In this paper, we predicted the rise or the fall in eleven major industrial stock price indices unlike existing studies dealing with the prediction of KOSPI that combines all industries. We used as input variables not only domestic economic indices but also foreign economic indices including the U.S.A, Japan, China and Europe that have affected korean stock market. Numerical analysis through SAS E-miner showed above or below about 60% accuracy using the logistic regression and neural network model.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.6-6
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• 2019

대마는 인류가 이용해 온 가장 오래된 약제 중 하나로 그 원산지는 중앙아시아와 남아시아이다. 식물분류학적으로 대마속 일년생 식물로서 Cannabis sativa, Cannabis indica, Cannabis ruderalis 3종이 있으며, 우리나라에서 재배되고 있는 종은 Cannabis sativa이다. 대마 재배의 역사는 인류의 시작과 그 궤를 같이하며, 동 서양을 막론하고 고대 문명에서 대마에 대한 기록을 쉽게 찾아볼 수 있다. 기록에 의하면 병의 치료나 심리적 치유 및 신에게 제사를 올릴 때 제사장이 사용한 것으로 알려져 있다. 대마의 약효에 대하여는 B.C. 2737년 중국의 신농황제시대의 기록에 관절염과 통증등 의료목적으로 사용했던 최초의 기록이 있으며 본초강목과 동의보감에 저술되어 있다. 우리나라의 대마에 관한 문헌 기록은 삼국지 '위지동이전', 삼국사기 '동성왕편'과 삼국유사에 삼베를 사용한 기록이 있는 것으로 미루어 봤을 때 대마재배의 역사는 삼국시대 이전으로 볼 수 있다. 우리 민족은 생활 속에서 대마를 즐겨 사용하였으며 삼베로 의복과 멍석, 행주 그리고 칠공예품이나 신발등을 만들어 사용하였으며, 죽음에 이르러 삼베옷을 수의로 사용하였다. 대마의 용도는 뿌리, 줄기, 잎, 꽃대 그리고 씨앗까지 다양하게 이용된다. 전통적으로 줄기의 껍질을 이용한 섬유제품이 있으며 실, 의복 및 밧줄등이 있다. 대마 줄기의 속대는 종이, 건축자재, 연료로 사용된다. 씨앗의 경우 식품과 조류의 먹이, 생약으로 이용되고 씨앗의 기름은 연료, 화장품, 맛사지 오일등으로 사용되고 있다. 환각성분이 있어 마리화나 원료로 사용되는 꽃대와 잎은 의약품의 원료로 주목받고 있다. 대마에 관한 최초의 논문은 1843년에 Cannabis indica의 약효에 관한 것으로 보고되었다. 1850년부터 1937년까지 미국의 약전은 대마를 100가지 이상의 질병에 효과가 있는 주요 의약품으로 기재하고 있다. 세계적으로 여러 가지 이유로 대마를 의료 응용과 연구 및 사용을 제한하여 대마에 관한 연구가 침체되었다. 대마의 의학연구는 대마의 약효성분인 칸나비노이드의 발견과 그 구조 및 약효에 관한 연구가 시작되면서 1960년대부터 증가하였으며 2000년 이후에는 칸나비노이드 및 칸나비디올의 다양한 의학적 효과가 밝혀지면서 급격히 증가하고 있다. 대마에 포함된 성분의 의학적 효과가 입증되면서 대마 사용을 합법화한 국가가 증가하면서 대마 산업이 급부상하고 있으며, 의료용뿐만 아니라 기호용, 식품용, 그리고 주류 및 음료시장까지 확대되고 있다. 우리나라도 2019년 3월 질병 치료 목적 대마성분 의약품을 제한적으로 허용하는 마약류 관리에 관한 법률 일부 개정안이 시행되면서 의료용 대마에 관한 연구와 산업화에 관심이 증가하는 추세이다.